Hitt, K.J.2007Vulnerability of shallow ground water and drinking-water wells
to nitrate in the United States: Model of predicted nitrate
concentration in shallow, recently recharged ground water --
Input data set for drainage ditch (gwava-s_ddit)
1Raster digital dataReston, Virginia, USAU.S. Geological Surveyhttp://water.usgs.gov/lookup/getspatial?gwava-s_dditNolan, B.T.Hitt, K.J.2006
Vulnerability of shallow ground water and drinking-water wells
to nitrate in the United States
Environmental Science and TechnologyVolume 40, Number 24, pages 7834-7840http://water.usgs.gov/nawqa/nutrients/pubs/est_v40_no24/http://pubs3.acs.org/acs/journals/doilookup?in_doi=10.1021/es060911u
This data set represents the area of National Resources
Inventory surface drainage, field ditch conservation practice,
in square kilometers, in the conterminous United States.
The data set was used as an input data layer for a national
model to predict nitrate concentration in shallow ground water.
Nolan and Hitt (2006) developed two national models to predict
contamination of ground water by nonpoint sources of
nitrate. The nonlinear approach to national-scale Ground-WAter
Vulnerability Assessment (GWAVA) uses components representing
nitrogen (N) sources, transport, and attenuation.
One model (GWAVA-S) predicts nitrate contamination of shallow
(typically less than 5 meters deep), recently recharged ground
water, which may or may not be used for drinking. The other
(GWAVA-DW) predicts ambient nitrate concentration in deeper
supplies used for drinking.
This data set is one of 17 data sets (1 output data set and 16
input data sets) associated with the GWAVA-S model. Full details
of the model development are in Nolan and Hitt (2006).
For inputs to the model, spatial attributes representing 16
nitrogen loading and transport and attenuation factors were
compiled as raster data sets (1-km by 1-km grid cell size) for
the conterminous United States (see table 1).
>Table 1.-- Parameters of nonlinear regression model for nitrate in shallow
> ground water (GWAVA-S) and corresponding input spatial data sets.
> [kg, kilograms; km2, square kilometers.]
>
>Nitrogen Source Factors Data Set Name
> 1 farm fertilizer (kg/hectare) gwava-s_ffer
> 2 confined manure (kg/hectare) gwava-s_conf
> 3 orchards/vineyards (percent) gwava-s_orvi
> 4 population density (people/km2) gwava-s_popd
> 5 cropland/pasture/fallow (percent) gwava-s_crpa
>
>Transport to Aquifer Factors
> 6 water input (km2/cm) gwava-s_wtin
> 7 carbonate rocks (yes/no) gwava-s_crox
> 8 basalt and volcanic rocks (yes/no) gwava-s_vrox
> 9 drainage ditch (km2) gwava-s_ddit
> 10 slope (percent x 1000) gwava-s_slop
> 11 glacial till (yes/no) gwava-s_gtil
> 12 clay sediment (percent x 1000) gwava-s_clay
>
>Attenuation Factors
> 13 fresh surface water withdrawal gwava-s_swus
> for irrigation (megaliters/day)
> 14 irrigation tailwater recovery (km2) gwava-s_twre
> 15 histosol soil type (percent) gwava-s_hist
> 16 wetlands (percent) gwava-s_wetl
"Farm fertilizer" is the average annual nitrogen input from
commercial fertilizer applied to agricultural lands, 1992-2001, in
kilograms per hectare.
"Confined manure" is the average annual nitrogen input from
confined animal manure, 1992 and 1997, in kilograms per
hectare.
"Orchards/vineyards" is the percent of orchards/vineyards land
cover classification.
"Population density" is 1990 block group population density, in
people per square kilometer.
"Cropland/pasture/fallow" is the percent of
cropland/pasture/fallow land cover classifications.
"Water input" is the ratio of the total area of irrigated land
to precipitation, in square kilometers per centimeter.
"Carbonate rocks" is the presence or absence of Valley and Ridge
carbonate rocks.
"Basalt and volcanic rocks" is the presence or absence of basalt
and volcanic rocks.
"Drainage ditch" is the area of National Resources Inventory surface
drainage, field ditch conservation practice, in square kilometers.
"Slope" is the soil surface slope, in percent times 1000.
"Glacial till" is the presence or absence of poorly sorted
glacial till east of the Rocky Mountains.
"Clay sediment" is the amount of clay sediment in the soil, in
percent times 1000.
"Fresh surface water withdrawal for irrigation" is the amount of
fresh surface water withdrawal for irrigation, in megaliters per day.
"Irrigation tailwater recovery" is the area of National
Resources Inventory irrigation system, tailwater recovery
conservation practice, in square kilometers.
"Histosol soil type" is the amount of histosols soil taxonomic
order, in percent.
"Wetlands" is the percent of woody wetlands and emergent
herbaceous wetlands land cover classifications.
Reference cited:
Nolan, B.T. and Hitt, K.J., 2006, Vulnerability of shallow
ground water and drinking-water wells to nitrate in the United
States: Environmental Science and Technology, vol. 40, no. 24,
pages 7834-7840.
This particular data layer was created to help characterize
nitrogen transport factors at a national level for input to a national
model to predict nitrate concentration in shallow ground water.
Nitrate is considered to be the most widespread contaminant in
ground water. High nitrate concentration in ground water is a
concern for human health, and protecting drinking water sources
is a national priority. The U.S. Geological Survey's National
Water-Quality Assessment (NAWQA) Program monitors the occurrence
and distribution of nitrate and other contaminants in ground
water and streams. However, because monitoring everywhere for
the occurrence and distribution of nitrate in ground water is
impractical, national water-quality models are used to address
data gaps. The goal of the current study was to predict ground
water vulnerability to nitrate at the national scale, to
complement measured data.
The data set is provided as native ESRI ArcInfo Workstation GRID
and as ASCII text (plain text) format.
The file "gridname".tgz file contains the GRID in a directory
(folder) called arctar00000, where "gridname" is the name of the
data set. For example, a GRID without a VAT (value attribute
table) has the following files:
>arctar00000/
>arctar00000/gridname/
>arctar00000/gridname/dblbnd.adf
>arctar00000/gridname/hdr.adf
>arctar00000/gridname/log
>arctar00000/gridname/metadata.xml
>arctar00000/gridname/prj.adf
>arctar00000/gridname/sta.adf
>arctar00000/gridname/w001001.adf
>arctar00000/gridname/w001001x.adf
>arctar00000/info/
>arctar00000/info/arc.dir
>arctar00000/info/arc0000.dat
>arctar00000/info/arc0000.nit
>arctar00000/info/arc0001.dat
>arctar00000/info/arc0001.nit
>arctar00000/log
A GRID with a VAT (value attribute table) (gwava-s_orvi,
gwava-s_crpa, gwava-s_crox, gwava-s_vrox, gwava-s_slop,
gwava-s_gtil, gwava-s_clay, gwava-s_hist, gwava-s_wetl) has
these files:
>arctar00000/
>arctar00000/gridname/
>arctar00000/gridname/dblbnd.adf
>arctar00000/gridname/hdr.adf
>arctar00000/gridname/log
>arctar00000/gridname/metadata.xml
>arctar00000/gridname/prj.adf
>arctar00000/gridname/sta.adf
>arctar00000/gridname/vat.adf
>arctar00000/gridname/w001001.adf
>arctar00000/gridname/w001001x.adf
>arctar00000/gridname.aux
>arctar00000/info/
>arctar00000/info/arc.dir
>arctar00000/info/arc0000.dat
>arctar00000/info/arc0000.nit
>arctar00000/info/arc0001.dat
>arctar00000/info/arc0001.nit
>arctar00000/info/arc0002.dat
>arctar00000/info/arc0002.nit
>arctar00000/info/arc0002r.001
>arctar00000/log
To extract the ArcInfo Workstation GRID from the "gridname".tgz archive
file, use TARARC, WINZIP, or the following commands:
>gunzip gridname.tgz
>tar xvof gridname.tar
The data set is provided in ASCII text format in addition to the
native ESRI ArcInfo Workstation GRID format in case the user's
software cannot access the data in ArcInfo Workstation GRID
format.
The ASCII file is compressed using gzip as "gridname".txt.gz,
where "gridname" is the data set name.
19912003
Water-quality data used in this study were collected during
1991-2003 and represent the first full decade of sampling by the
NAWQA program. The input data layers describe conditions in the
mid 1990's, and so the predictions represent mid 1990's land-use
and nitrogen-loading conditions.
CompleteNone planned.-128.30785909-65.1433869651.85798422.73659812NoneGround waterGround water contaminationGround water pollutionGround water susceptibilityNutrientsNitrateNational Water-Quality Assessment ProgramNAWQANonlinear modelNitrate concentrationinlandWatersDrainage ditchNRINational Resources InventoryNational Land Cover DataNoneConterminous United StatesNoneNoneNoneNone
None.
This spatial data set is one of a group of data sets developed
specifically for use in a national model of nitrate in ground
water. The data set should be used at the national or regional
level, not at the local level.
Users should consider the various assumptions that went into
generating each spatial data set and the limitations inherent in
the source data materials in deciding whether the data set is
appropriate for use in other national or regional applications.
Please acknowledge the U.S. Geological Survey in products derived
from these data.
Kerri Hitt.U.S. Geological SurveyHydrologistmailing addressMS-413 National Center12201 Sunrise Valley DriveRestonVA20192USA703 648-6854khitt@usgs.govPlease contact thru emailhttp://water.usgs.gov/GIS/browse/gwava-dw_out.jpg
Map of nitrate concentration in U.S. ground water used for drinking,
as predicted by the GWAVA-DW model.
jpg
Thanks to Nancy T. Baker who reviewed the metadata
and provided useful comments.
Microsoft Windows XP Version 5.1 (Build 2600)
Service Pack 2; ESRI ArcCatalog 9.0.0.535
Nolan, B.T.2001
Relating nitrogen sources and aquifer susceptibility to
nitrate in shallow ground waters of the United States
1Ground WaterVolume 39, Number 2, pages 290-299http://water.usgs.gov/nawqa/nutrients/pubs/gw_v39_no2/Nolan, B.T., Hitt, K.J., and Ruddy, B.C.2002
Probability of nitrate contamination of recently recharged groundwaters
in the conterminous United States
1Raster digital dataEnvironmental Science & TechnologyVolume 36, Number 10, pages 2138-2145http://water.usgs.gov/nawqa/nutrients/pubs/est_v36_no10/http://water.usgs.gov/lookup/getspatial?gwriskNolan, B.T.Hitt, K.J.2006
Vulnerability of shallow ground water and drinking-water wells
to nitrate in the United States
Environmental Science and TechnologyVolume 40, Number 24, pages 7834-7840Denver, ColoradoU.S. Geological Surveyhttp://water.usgs.gov/nawqa/nutrients/pubs/est_v40_no24/http://pubs3.acs.org/acs/journals/doilookup?in_doi=10.1021/es060911uHitt, K.J.2007
Vulnerability of shallow ground water and drinking-water wells
to nitrate in the United States: Model of predicted nitrate concentration
in shallow, recently recharged ground water -- Model output data set (gwava-s_out)
1Raster digital dataReston, Virginia, USAU.S. Geological Surveyhttp://water.usgs.gov/lookup/getspatial?gwava-s_outHitt, K.J.2007
Vulnerability of shallow ground water and drinking-water wells
to nitrate in the United States: Model of predicted nitrate concentration
in shallow, recently recharged ground water -- Input data set for farm fertilizer (gwava-s_ffer)
1Raster digital dataReston, Virginia, USAU.S. Geological Surveyhttp://water.usgs.gov/lookup/getspatial?gwava-s_fferHitt, K.J.2007
Vulnerability of shallow ground water and drinking-water wells
to nitrate in the United States: Model of predicted nitrate concentration
in shallow, recently recharged ground water -- Input data set for confined manure (gwava-s_conf)
1Raster digital dataReston, Virginia, USAU.S. Geological Surveyhttp://water.usgs.gov/lookup/getspatial?gwava-s_confHitt, K.J.2007
Vulnerability of shallow ground water and drinking-water wells
to nitrate in the United States: Model of predicted nitrate concentration
in shallow, recently recharged ground water -- Input data set for orchards/vineyards (gwava-s_orvi)
1Raster digital dataReston, Virginia, USAU.S. Geological Surveyhttp://water.usgs.gov/lookup/getspatial?gwava-s_orviHitt, K.J.2007
Vulnerability of shallow ground water and drinking-water wells
to nitrate in the United States: Model of predicted nitrate concentration
in shallow, recently recharged ground water -- Input data set for population density (gwava-s_popd)
1Raster digital dataReston, Virginia, USAU.S. Geological Surveyhttp://water.usgs.gov/lookup/getspatial?gwava-s_popdHitt, K.J.2007
Vulnerability of shallow ground water and drinking-water wells
to nitrate in the United States: Model of predicted nitrate concentration
in shallow, recently recharged ground water -- Input data set for cropland/pasture/fallow (gwava-s_crpa)
1Raster digital dataReston, Virginia, USAU.S. Geological Surveyhttp://water.usgs.gov/lookup/getspatial?gwava-s_crpaHitt, K.J.2007
Vulnerability of shallow ground water and drinking-water wells
to nitrate in the United States: Model of predicted nitrate concentration
in shallow, recently recharged ground water -- Input data set for water input (gwava-s_wtin)
1Raster digital dataReston, Virginia, USAU.S. Geological Surveyhttp://water.usgs.gov/lookup/getspatial?gwava-s_wtinHitt, K.J.2007
Vulnerability of shallow ground water and drinking-water wells
to nitrate in the United States: Model of predicted nitrate concentration
in shallow, recently recharged ground water -- Input data set for carbonate rocks (gwava-s_crox)
1Raster digital dataReston, Virginia, USAU.S. Geological Surveyhttp://water.usgs.gov/lookup/getspatial?gwava-s_croxHitt, K.J.2007
Vulnerability of shallow ground water and drinking-water wells
to nitrate in the United States: Model of predicted nitrate concentration
in shallow, recently recharged ground water -- Input data set for basalt and volcanic rocks (gwava-s_vrox)
1Raster digital dataReston, Virginia, USAU.S. Geological Surveyhttp://water.usgs.gov/lookup/getspatial?gwava-s_vroxHitt, K.J.2007
Vulnerability of shallow ground water and drinking-water wells
to nitrate in the United States: Model of predicted nitrate concentration
in shallow, recently recharged ground water -- Input data set for slope (gwava-s_slop)
1Raster digital dataReston, Virginia, USAU.S. Geological Surveyhttp://water.usgs.gov/lookup/getspatial?gwava-s_slopHitt, K.J.2007
Vulnerability of shallow ground water and drinking-water wells
to nitrate in the United States: Model of predicted nitrate concentration
in shallow, recently recharged ground water -- Input data set for glacial till (gwava-s_gtil)
1Raster digital dataReston, Virginia, USAU.S. Geological Surveyhttp://water.usgs.gov/lookup/getspatial?gwava-s_gtilHitt, K.J.2007
Vulnerability of shallow ground water and drinking-water wells
to nitrate in the United States: Model of predicted nitrate concentration
in shallow, recently recharged ground water -- Input data set for clay sediment (gwava-s_clay)
1Raster digital dataReston, Virginia, USAU.S. Geological Surveyhttp://water.usgs.gov/lookup/getspatial?gwava-s_clayHitt, K.J.2007
Vulnerability of shallow ground water and drinking-water wells
to nitrate in the United States: Model of predicted nitrate concentration
in shallow, recently recharged ground water -- Input data set for fresh surface water withdrawal (gwava-s_swus)
1Raster digital dataReston, Virginia, USAU.S. Geological Surveyhttp://water.usgs.gov/lookup/getspatial?gwava-s_swusHitt, K.J.2007
Vulnerability of shallow ground water and drinking-water wells
to nitrate in the United States: Model of predicted nitrate concentration
in shallow, recently recharged ground water -- Input data set for irrigation tailwater recovery (gwava-s_twre)
1Raster digital dataReston, Virginia, USAU.S. Geological Surveyhttp://water.usgs.gov/lookup/getspatial?gwava-s_twreHitt, K.J.2007
Vulnerability of shallow ground water and drinking-water wells
to nitrate in the United States: Model of predicted nitrate concentration
in shallow, recently recharged ground water -- Input data set for histosols (gwava-s_hist)
1Raster digital dataReston, Virginia, USAU.S. Geological Surveyhttp://water.usgs.gov/lookup/getspatial?gwava-s_histHitt, K.J.2007
Vulnerability of shallow ground water and drinking-water wells
to nitrate in the United States: Model of predicted nitrate concentration
in shallow, recently recharged ground water -- Input data set for wetlands (gwava-s_wetl)
1Raster digital dataReston, Virginia, USAU.S. Geological Surveyhttp://water.usgs.gov/lookup/getspatial?gwava-s_wetlHitt, K.J.2007
Vulnerability of shallow ground water and drinking-water wells
to nitrate in the United States: Model of predicted nitrate
concentration in U.S. ground water used for drinking
(simulation depth 50 meters) -- Model output data set (gwava-dw_out)
1Raster digital dataReston, Virginia, USAU.S. Geological Surveyhttp://water.usgs.gov/lookup/getspatial?gwava-dw_outNakagaki, NaomiWolock, David M.2005
Estimation of agricultural pesticide use in drainage basins
using land cover maps and county pesticide data
1U.S. Geological Survey Open-File Report2005-1188http://pubs.usgs.gov/of/2005/1188/
The model results were checked using standard USGS review
procedures.
Not applicable for raster data.
The data spans the conterminous United States.
An output cell was assigned a value of "no data" if any of the
corresponding input data cells at that location was "no data."
U.S. Department of Agriculture (USDA)19951992 National Resources InventoryVersion 1.0tablesWashington, DC and Ames, IA
Natural Resources Conservation Service and
Iowa State University Statistical Laboratory
http://www.nrcs.usda.gov/technical/nri/1997/obtain_data.htmlCDROM19921992 NRINRI 92
The National Resources Inventory (NRI) is a statistical survey
of land use and natural resource conditions and trends on
non-Federal lands of the United States and Puerto Rico. Data
are collected on soil characteristics, land use, land cover,
wind erosion, water erosion, and conservation practices.
The area (km2) of land on which each NRI conservation practice
was applied was aggregated by county.
Vogelmann, J.E.Howard, S.M.Yang, L.Larson, C.R.Wylie, B.K.Van Driel, N.2001
Completion of the 1990s National Land Cover Dataset for the
conterminous United States from Landsat Thematic Mapper data and
ancillary data sources
raster digital data
Photogrammetric Engineering and Remote Sensing
Journal of the American Society for Photogrammetry and Remote Sensing
v. 67, no. 6, p. 650-662http://www.mrlc.gov/nlcd2k1_product_desc.asphttp://www.asprs.org/publications/pers/2001journal/june/highlight.htmlOn-line1992Source imageryNLCD 92
NLCD 92 (National Land Cover Dataset 1992) is a 21-category
land cover classification scheme that has been applied
consistently over the conterminous United States. It is based
primarily on the unsupervised classification of Landsat TM
(Thematic Mapper) 1992 imagery. Ancillary data sources
included topography, census, agricultural statistics, soil
characteristics, other land cover maps, and wetlands data. The
NLCD 92 classification is provided as raster data with a
spatial resolution of 30 meters.
Price, CurtisNakagaki, NaomiHitt, KerieClawges, Rick2007
Enhanced historical land-use and land-cover data sets
of the U.S. Geological Survey
Version 1.0mapU.S Geological Survey Data Series240http://pubs.usgs.gov/ds/2006/240/On-line19701985ground conditionUSGS DS 240
A 30-m spatial resolution version of this land use and land
cover (LULC) data set was used to develop an enhanced version
of the NLCD 92 (called NLCDE 92) at 30-meter and 1-kilometer
resolutions using methods described in:
Nakagaki, N., and Wolock, D.M., 2005, Estimation of
agricultural pesticide use in drainage basins using land cover
maps and county pesticide data: U.S. Geological Survey
Open-File Report 2005-1188, pp.4-10.
The LULC data were derived from aerial photography, and NLCD
92 data were derived from satellite imagery. The NLCD was
modified with selected land-use categories of LULC, as
described in Nakagaki and Wolock (2005), because LULC data are
a better source for some land categories that are difficult to
distinguish using only satellite imagery (residential,
orchards/vineyards/other, and tundra). The enhanced NLCD
(NLCDE 92) includes 21 land-cover classifications from the
original NLCD 92 plus an additional four categories from the
LULC (LULC tundra, NLCD/LULC forested residential, LULC
residential, and LULC orchards/vineyards/other) (Nakagaki and
Wolock, 2005).
The 30-m resolution NLCDE 92 was used to create a set of 25
1-km resolution national grids of "percentage" land cover (one
grid for each land cover class) using methods described in
Nakagaki and Wolock (2005), pages 10-13.
In each 1-km resolution national grid, the value of a grid
cell was the percentage of the 1 km by 1 km area specific to
that land cover class. For example, the grid of "pasture/hay"
indicated the percentage of pasture/hay in each cell, and the
grid of "row crops" indicated the percentage of row crops in
each cell.
Spatial data sets representing nitrogen (N) loading and
transport and attenuation factors were compiled for the
conterminous United States.
N source variables include farm fertilizer, manure from
confined animal feeding operations, and loading surrogates
that reflect additional sources of N. For example, population
density is a surrogate for nonagricultural sources of N from
septic tanks, sewers, and domestic animal waste in urban
areas.
Transport factors include water input, sediments, rock type,
and selected management practices. "Water input" is an
interaction term expressed as the ratio of the total area of
irrigated land to precipitation. Attenuation factors include
variables that are surrogates for dilution and/or
denitrification.
Each of the variables in the model was compiled within 1-km by
1-km grid cells for prediction of nitrate concentration at the
national scale.
>Table 2.-- Variables compiled for regression model GWAVA-S,
> units and estimated coefficients
> [kg, kilograms; km2, square kilometers.]
>
>Nitrogen Loading Units Estimated coefficient
> 1 farm fertilizer kg/hectare 0.2265
> 2 confined manure kg/hectare 0.4049
> 3 orchards/vineyards percent 1.9600
> 4 population density people/km2 0.006658
> 5 cropland/pasture/fallow percent 0.1473
>
>Transport Factors
> 6 water input km2/cm 38.16
> 7 carbonate rocks presence/absence 0.5630
> 8 basalt and volcanic rocks presence/absence 0.5182
> 9 drainage ditch km2 -6.483
> 10 slope percent* -0.03861
> 11 glacial till presence/absence -0.8141
> 12 clay sediment percent* -0.04751
>
>Attenuation Factors
> 13 fresh surface water withdrawal megaliters/day -1.078
> 14 irrigation tailwater recovery km2 -8.327
> 15 histosol soil type percent -0.0185
> 16 wetlands percent -0.03213
>
>*Values in the SLOPE and CLAY grids were multiplied by 1000 to convert
> the grids from floating point to integer.
200512
A national grid of the NRI conservation practice (drainage
ditch) was generated by adding a spatial component to tabular
county estimates of the NRI practice (USDA, 1995). Mapped
agricultural land cover from NLCDE 92 1-km resolution GRIDS
was used to spatially refine the geographic location of the
NRI conservation practice from countywide tables to
agricultural land within a county.
In each NLCDE 92 1-km resolution national grid, the value of a
grid cell was the percentage of the 1 km by 1 km area specific
to that land cover class. If a 1-km2 cell contained 78
percent agricultural land, this translated to .78 km2 of
agricultural land in the cell.
Agricultural land where NRI practices were applied was defined
as the sum of these six percentage land cover classifications
from NLCDE 92:
>Code Land cover classification
> 61 Orchards/vineyards/other
> 62 LULC orchards/vineyards/other
> 81 Pasture/hay
> 82 Row crops
> 83 Small grains
> 84 Fallow
The assumption was that NRI conservation practices were
applied equally to these land cover categories. The NRI
covers only non-federal land, but the procedure did not
account for this.
A 1-km resolution national grid of "surface drainage, field
ditch" conservation practice (NRI CODE 607) was generated
using steps similar to those used to generate a 1-km
resolution pesticide use grid described in Nakagaki and Wolock
(2005), pages 17-20. The steps were:
1. Compute the county area of NLCDE 92 agricultural land cover
classifications in each county in the conterminous United
States.
a. The 1-km grid of percent values of agricultural land
was converted to a grid of km2 of agricultural land by
dividing the value of each cell (percent) by 100.
b. The area of agricultural land was summed for each county
by overlaying the cell values (km2) of the areal grid with
a 1-km resolution county grid.
2. Compute the county "intensity" (or "rate") of drainage
ditch areas for each county in the conterminous United
States.
The "intensity" was computed by dividing the drainage ditch
area of the county (km2) by the county area of agricultural
land computed in step 1b (km2).
The "intensity" data was linked to the 1-km national county
grid using county FIPS codes.
3. Create a national grid of estimated drainage ditch area.
The cell values of the areal grid of km2 of agricultural
land (step 1a) were multiplied by the cell values of the
areal grid of county drainage ditch area "intensity" (step
2). The cell values of the final grid represent the km2 of
drainage ditch area in each 1 km by 1 km cell.
Here is an example of how "surface drainage, field ditch" area
for one 1-km2 cell in Palm Beach County, Florida (FIPS 12099)
was generated:
>Step 1a. Percent of NLCDE 92 ag land in cell = 22, so the
>amount of NLCDE 92 ag land in cell = .22 km2
>
>Step 1b. Total area of NLCDE 92 ag land in county = 2215.47 km2
>
>Step 2. Total surface drainage ditch in county = 1445.552 km2
>(from USDA, 1995)
>
>County "intensity" = 1445.552 km2 / 2215.47 km2 = .6524
>
>Step 3. Amount of drainage ditch in cell = .22 km2 * .6524 =
>.1435 km2
>The sum of drainage ditch area calculated for all cells in
>Palm Beach County, Florida (FIPS 12099) equals 1445.552 km2.
These steps were repeated for all cells nationwide to generate
a 1-km resolution national grid. Each cell value represented
the amount (km2) of surface drainage, field ditch in the cell.
This method does not account for the possibility that the
original NRI estimate of agricultural land could be more than
the NLCDE 92 mapped agricultural land. In such a case, the
grid cell value representing the area of the NRI practice
could be more than the NLCDE ag area or more than the total
area of the 1-km2 grid cell.
200512
To make the national map of predicted nitrate concentration,
the values from the 1-km by 1-km grid cells for each of the
input data layers were put in to the model equation to
calculate a predicted concentration for each output cell. An
output cell was assigned a value of "no data" if any of the
corresponding input data cells at that location was "no data."
The following ArcInfo Workstation GRID commands produced the
predicted nitrate concentration grid:
>/*Nonlinear regression model 1 for nitrate in shallow ground water
>/*Dec 23, 2005
>setwindow -2380000.000 260000.000 2265000.000 3200000.000 gwava-s_orvi
>setcell 1000
>/*Set directory housing the filtered input grids for fertilizer, manure, population, and water input.
>&s fdir = d:/ancill/model2005/dec2005/filtered
>/*
>/*Use filtered N input grids in kg/hectare.
>/*
>gwava-s_out = ((0.226544 * %fdir%/gwava-s_ffer) + (0.404948 * %fdir%/gwava-s_conf) + (1.959994 * gwava-s_orvi) + (0.006658 * gwava-s_popd) + ~
>(0.147298 * gwava-s_crpa)) * ~
>exp ((0.562985 * gwava-s_crox) + (0.518172 * gwava-s_vrox) + (-6.48311 * gwava-s_ddit) + (-0.03861 * ((gwava-s_slop / 1000))) + ~
>(-0.81412 * gwava-s_gtil) + (-0.04751 * (gwava-s_clay / 1000)) + (38.16395 * %fdir%/gwava-s_wtin)) * ~
>exp ((-1.07756 * gwava-s_swus) + (-8.3267 * gwava-s_twre) + (-0.01846 * gwava-s_hist) + (-0.03213 * gwava-s_wetl))
GRID cells were randomly selected from this data set and were
checked by hand to ensure the correct values were calculated
using the model equation and the input data layers.
Areas with high N load, low to moderate clay content,
sufficient water input, and low denitrification potential have
the highest predicted nitrate concentration in ground water
and therefore may be vulnerable to nitrate contamination. The
most extensive areas of predicted, severe contamination
(nitrate greater than 10 mg/L) occur in the High Plains, and
areas of predicted, moderate contamination (more than 5 to 10
mg/L nitrate) occur extensively in the northern Midwest.
200512
The input GRID was converted to ASCII (plain text) for
distribution using ArcInfo Workstation command:
>gridascii gwava-s_ddit gwava-s_ddit.txt
2007RasterGrid Cell294046451Albers Conical Equal Area29.545.5-96230.000000.00000row and column1000.01000.0METERSNorth American Datum of 1983GRS806378137.000000294.257222
Each 1-km by 1-km grid cell stores the area of NRI conservation
practice 607, surface drainage, field ditch, in square
kilometers.
Data Type: Floating Point
Minimum Value = 0.000
Maximum Value = 1.161
Mean = 0.013
Standard Deviation = 0.066
The grid is floating point; a VAT is not present.
In the ASCII text file, "no data" is indicated as -9999.
None.U.S. Geological SurveyAsk USGS -- Water Webserver Teammailing445 National CenterRestonVA201921-888-275-8747 (1-888-ASK-USGS)http://answers.usgs.gov/cgi-bin/gsanswers?pemail=h2oteam&subject=GIS+Dataset+gwava-s_ddit
Although this data set has been used by the U.S. Geological Survey,
U.S. Department of the Interior, no warranty expressed or implied
is made by the U.S. Geological Survey as to the accuracy of the
data and related materials. The act of distribution shall not
constitute any such warranty, and no responsibility is assumed by
the U.S. Geological Survey in the use of this data, software, or
related materials.
Any use of trade, product, or firm names is for descriptive
purposes only and does not imply endorsement by the U.S.
Government.
ArcInfo Workstation GRIDgzip -d; gunzip4279400 byteshttp://water.usgs.gov/GIS/dsdl/gwava-s/arctar/gwava-s_ddit.tgzASCII filegzip -d; gunzip5413900 byteshttp://water.usgs.gov/GIS/dsdl/gwava-s/gascii/gwava-s_ddit.txt.gz
Index to all files related to the GWAVA-S model to facilitate downloading
all the GIS data sets
Web page with links to all data setsHTML5 byteshttp://water.usgs.gov/GIS/dsdl/gwava-s/index.htmlNone. This data set is provided by USGS as a public service.200704U.S. Geological SurveyAsk USGS -- Water Webserver Teammailing445 National CenterRestonVA201921-888-275-8747 (1-888-ASK-USGS)http://water.usgs.gov/user_feedback_form.htmlFGDC Content Standards for Digital Geospatial MetadataFGDC-STD-001-1998